Hindawi Publishing Corporation Advances in Materials Science and Engineering Volume 2016, Article ID 6429160, 8 pages http://dx.doi.org/10.1155/2016/6429160
Research Article Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material Adel T. Abbas,1 Karim Hamza,2 Mohamed F. Aly,3 and Essam A. Al-Bahkali1 1
Department of Mechanical Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48105, USA 3 Department of Mechanical Design, American University in Cairo, New Cairo, Cairo 11835, Egypt 2
Correspondence should be addressed to Adel T. Abbas;
[email protected] Received 11 January 2016; Revised 20 March 2016; Accepted 23 March 2016 Academic Editor: Wenbin Yi Copyright © 2016 Adel T. Abbas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel) with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut) that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The tradeoff among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO) algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.
1. Introduction Heat-treated alloy steels exhibit many attractive properties, such as wear resistance, high strength, and high thermal stability. Examples of uses of heat-treated alloy steels include dies, automotive structures, bearings, and gears. In a highly competitive world, there is much need for optimizing the manufacturing and processing technologies to achieve highquality products at low cost and high productivity. Focus of this research is on optimizing the cutting parameters of CNCturning operations of J-Steel material in order to achieve (i) high-quality surface finish (which translates to product quality) and (ii) high material removal rate (which translates to productivity). Cost is not modeled in this study due to it being (at least partially) correlated with productivity and due to several uncertainties that can be site-specific (e.g., cost of labor, energy, and transport). Examples of notable related work in the literature of machinability of other types of hardened steels include [1–3],
and a review of state of the art and identification of active research areas is summarized in [4]. The primary control parameters (also termed “design variables” within an optimization framework) in CNC turning are the machining parameters (cutting speed, feed rate, and depth of cut), material, and geometry of the cutting tools and cutting fluids. Hard-turning operations require no cutting fluids, which is advantageous in terms of cost (purchase and disposal of cutting fluids), environmental impact (elimination of the need to posttreat the cutting fluids for safe disposal), and process tenability (fewer parameters to adjust). By selecting a set of standard Tungsten-Carbide insert tools, thereby considering the tooling as a fixed parameter, this study conducts a thorough examination of the three machining parameters and how they relate to the trade-off between surface quality and material removal rate. Tuning of the cutting parameters is conducted in this paper within the context of Pareto-optimality in order to find good compromises between surface quality and process
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Figure 1: Test rig for turning and surface roughness measurement.
productivity. The tuning is done via “dense sampling” (fivelevel full-factorial design of experiments) as well as multiobjective implementation of an optimization technique from metamodeling based design optimization, “Efficient Global Optimization” (EGO) [5–8]. EGO seeks to locate the optimum of a processes while conducting as few experiments as possible. This is done via incrementally updating a Gaussian process metamodel that approximates the behavior of the actual process. Multiobjective versions of EGO such as ParEGO [9] and m-EGO, which is some of the authors’ earlier work in [10, 11], have been proposed and tested via numerical models. To the best of the authors’ knowledge, the present study is the first experimentation with utilizing m-EGO in real machining experiments. This paper started with a brief introduction and motivation of the performed research. The rest of the paper is organized as follows. Section 2 provides details of the experimental setup and material to be machined. Section 3 showcases the results from a five-level full-factorial experimental matrix (125 samples). Section 4 presents the mEGO approach for identification of the Pareto trade-off frontier. Section 5 discusses the results and implications from both approaches, and then the paper concludes with a brief summary.
2. Experimental Setup EMCO Concept Turn 45 CNC lathe equipped with Sinumeric 840-D was used to conduct experimental work. An uncoated Tungsten-Carbide insert was clamped with the tool holder to carry out this work. The specifications for insert and tool holder are TR-V13JBL 2020K and TR-VB1304-F. Clearance angle, cutting edge angle, and nose radius are maintained by 7∘ , 75∘ , and 0.4 mm, respectively. After machining, a surface roughness tester TESA was used to measure the surface roughness over a 50 mm length of the machined surface. Photos of the test rig are shown in Figure 1. Chemical composition of J-Steel material is listed in Table 1. The heat treatment for involved Austenitizing at 850∘ C for 4-5 hours, quenching in oil, tempered at 600–630∘ C for 6-7 hours and then air-cooled. Hardness was HV 365–395.
Table 1: Chemical composition of the J-Steel material. Element C Si Mn Ni Cr Mo V S P Minimum% 0.30 0.10 0.20 2.7 0.7 0.40
